# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501

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# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
# Augmentation (ADA)
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import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Function

from ..utils import ext_loader

ext_module = ext_loader.load_ext('_ext', ['fused_bias_leakyrelu'])


class FusedBiasLeakyReLUFunctionBackward(Function):
    """Calculate second order deviation.

    This function is to compute the second order deviation for the fused leaky
    relu operation.
    """

    @staticmethod
    def forward(ctx, grad_output: torch.Tensor, out: torch.Tensor,
                negative_slope: float, scale: float) -> tuple:
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale

        empty = grad_output.new_empty(0)

        grad_input = ext_module.fused_bias_leakyrelu(
            grad_output,
            empty,
            out,
            act=3,
            grad=1,
            alpha=negative_slope,
            scale=scale)

        dim = [0]

        if grad_input.ndim > 2:
            dim += list(range(2, grad_input.ndim))

        grad_bias = grad_input.sum(dim).detach()

        return grad_input, grad_bias

    @staticmethod
    def backward(ctx, gradgrad_input: torch.Tensor,
                 gradgrad_bias: nn.Parameter) -> tuple:
        out, = ctx.saved_tensors

        # The second order deviation, in fact, contains two parts, while the
        # the first part is zero. Thus, we direct consider the second part
        # which is similar with the first order deviation in implementation.
        gradgrad_out = ext_module.fused_bias_leakyrelu(
            gradgrad_input,
            gradgrad_bias.to(out.dtype),
            out,
            act=3,
            grad=1,
            alpha=ctx.negative_slope,
            scale=ctx.scale)

        return gradgrad_out, None, None, None


class FusedBiasLeakyReLUFunction(Function):

    @staticmethod
    def forward(ctx, input: torch.Tensor, bias: nn.Parameter,
                negative_slope: float, scale: float) -> torch.Tensor:
        empty = input.new_empty(0)

        out = ext_module.fused_bias_leakyrelu(
            input,
            bias,
            empty,
            act=3,
            grad=0,
            alpha=negative_slope,
            scale=scale)
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale

        return out

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor) -> tuple:
        out, = ctx.saved_tensors

        grad_input, grad_bias = FusedBiasLeakyReLUFunctionBackward.apply(
            grad_output, out, ctx.negative_slope, ctx.scale)

        return grad_input, grad_bias, None, None


class FusedBiasLeakyReLU(nn.Module):
    r"""Fused bias leaky ReLU.

    This function is introduced in the StyleGAN2:
    `Analyzing and Improving the Image Quality of StyleGAN
    <http://arxiv.org/abs/1912.04958>`_

    The bias term comes from the convolution operation. In addition, to keep
    the variance of the feature map or gradients unchanged, they also adopt a
    scale similarly with Kaiming initialization. However, since the
    :math:`1+{alpha}^2` is too small, we can just ignore it. Therefore, the
    final scale is just :math:`\sqrt{2}`. Of course, you may change it with
    your own scale.

    TODO: Implement the CPU version.

    Args:
        num_channels (int): The channel number of the feature map.
        negative_slope (float, optional): Same as nn.LeakyRelu.
            Defaults to 0.2.
        scale (float, optional): A scalar to adjust the variance of the feature
            map. Defaults to 2**0.5.
    """

    def __init__(self,
                 num_channels: int,
                 negative_slope: float = 0.2,
                 scale: float = 2**0.5):
        super().__init__()

        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return fused_bias_leakyrelu(input, self.bias, self.negative_slope,
                                    self.scale)


def fused_bias_leakyrelu(input: torch.Tensor,
                         bias: nn.Parameter,
                         negative_slope: float = 0.2,
                         scale: float = 2**0.5) -> torch.Tensor:
    r"""Fused bias leaky ReLU function.

    This function is introduced in the StyleGAN2:
    `Analyzing and Improving the Image Quality of StyleGAN
    <http://arxiv.org/abs/1912.04958>`_

    The bias term comes from the convolution operation. In addition, to keep
    the variance of the feature map or gradients unchanged, they also adopt a
    scale similarly with Kaiming initialization. However, since the
    :math:`1+{alpha}^2` is too small, we can just ignore it. Therefore, the
    final scale is just :math:`\sqrt{2}`. Of course, you may change it with
    your own scale.

    Args:
        input (torch.Tensor): Input feature map.
        bias (nn.Parameter): The bias from convolution operation.
        negative_slope (float, optional): Same as nn.LeakyRelu.
            Defaults to 0.2.
        scale (float, optional): A scalar to adjust the variance of the feature
            map. Defaults to 2**0.5.

    Returns:
        torch.Tensor: Feature map after non-linear activation.
    """
    assert scale > 0, 'The scale should be greater than 0'
    if not input.is_cuda and input.device.type != 'npu':
        return bias_leakyrelu_ref(input, bias, negative_slope, scale)

    return FusedBiasLeakyReLUFunction.apply(input, bias.to(input.dtype),
                                            negative_slope, scale)


def bias_leakyrelu_ref(x: torch.Tensor,
                       bias: nn.Parameter,
                       negative_slope: float = 0.2,
                       scale: float = 2**0.5) -> torch.Tensor:

    if bias is not None:
        assert bias.ndim == 1
        assert bias.shape[0] == x.shape[1]
        x = x + bias.reshape([-1 if i == 1 else 1 for i in range(x.ndim)])

    x = F.leaky_relu(x, negative_slope)
    if scale != 1:
        x = x * scale

    return x
